Proteomic and transcriptomic changes in hibernating grizzly bears reveal metabolic and signaling pathways that protect against muscle atrophy

Muscle atrophy is a physiological response to disuse and malnutrition, but hibernating bears are largely resistant to this phenomenon. Unlike other mammals, they efficiently reabsorb amino acids from urine, periodically activate muscle contraction, and their adipocytes differentially responds to insulin. The contribution of myocytes to the reduced atrophy remains largely unknown. Here we show how metabolism and atrophy signaling are regulated in skeletal muscle of hibernating grizzly bear. Metabolic modeling of proteomic changes suggests an autonomous increase of non-essential amino acids (NEAA) in muscle and treatment of differentiated myoblasts with NEAA is sufficient to induce hypertrophy. Our comparison of gene expression in hibernation versus muscle atrophy identified several genes differentially regulated during hibernation, including Pdk4 and Serpinf1. Their trophic effects extend to myoblasts from non-hibernating species (including C. elegans), as documented by a knockdown approach. Together, these changes reflect evolutionary favored adaptations that, once translated to the clinics, could help improve atrophy treatment

L-Tetrolet Pattern-Based Sleep Stage Classification Model Using Balanced EEG Datasets

Background: Sleep stage classification is a crucial process for the diagnosis of sleep or sleep-related diseases. Currently, this process is based on manual electroencephalogram (EEG) analysis, which is resource-intensive and error-prone. Various machine learning models have been recommended to standardize and automate the analysis process to address these problems. Materials and methods: The well-known cyclic alternating pattern (CAP) sleep dataset is used to train and test an L-tetrolet pattern-based sleep stage classification model in this research. By using this dataset, the following three cases are created, and they are: Insomnia, Normal, and Fused cases. For each of these cases, the machine learning model is tasked with identifying six sleep stages. The model is structured in terms of feature generation, feature selection, and classification. Feature generation is established with a new L-tetrolet (Tetris letter) function and multiple pooling decomposition for level creation. We fuse ReliefF and iterative neighborhood component analysis (INCA) feature selection using a threshold value. The hybrid and iterative feature selectors are named threshold selection-based ReliefF and INCA (TSRFINCA). The selected features are classified using a cubic support vector machine. Results: The presented L-tetrolet pattern and TSRFINCA-based sleep stage classification model yield 95.43%, 91.05%, and 92.31% accuracies for Insomnia, Normal dataset, and Fused cases, respectively. Conclusion: The recommended L-tetrolet pattern and TSRFINCA-based model push the envelope of current knowledge engineering by accurately classifying sleep stages even in the presence of sleep disorders.</jats:p

Hybrid deep feature generation for appropriate face mask use detection

Mask usage is one of the most important precautions to limit the spread of COVID-19. Therefore, hygiene rules enforce the correct use of face coverings. Automated mask usage classification might be used to improve compliance monitoring. This study deals with the problem of inappropriate mask use. To address that problem, 2075 face mask usage images were collected. The individual images were labeled as either mask, no masked, or improper mask. Based on these labels, the following three cases were created: Case 1: mask versus no mask versus improper mask, Case 2: mask versus no mask + improper mask, and Case 3: mask versus no mask. This data was used to train and test a hybrid deep feature-based masked face classification model. The presented method comprises of three primary stages: (i) pre-trained ResNet101 and DenseNet201 were used as feature generators; each of these generators extracted 1000 features from an image; (ii) the most discriminative features were selected using an improved RelieF selector; and (iii) the chosen features were used to train and test a support vector machine classifier. That resulting model attained 95.95%, 97.49%, and 100.0% classification accuracy rates on Case 1, Case 2, and Case 3, respectively. Having achieved these high accuracy values indicates that the proposed model is fit for a practical trial to detect appropriate face mask use in real time

Most complicated lock pattern-based seismological signal framework for automated earthquake detection

BACKGROUND : Seismic signals record earthquakes and also noise from different sources. The influence of noise makes it difficult to interpret seismograph signals correctly. This study aims to develop a computationally lightweight, accurate, and explainable machine learning model for the automated detection of seismogram signals that could serve as an effective warning system for earthquake prediction. MATERIAL AND METHOD : We developed a handcrafted model for earthquake detection using a balanced dataset of 5001 earthquakes and 5001 non-earthquake signal samples. The model included multilevel feature extraction, selectorbased feature selection, classification, and post-processing. Input signals were decomposed using tunable Q wave transform and fed to a statistical and textural feature extractor based on the most complicated lock pattern (MCLP). Four feature selectors were used to choose the most valuable features for the support vector machine classifier. Additionally, voted vectors were generated using iterative hard majority voting. Finally, the best model was chosen using a greedy algorithm. RESULTS : The presented self-organized MCLP-based feature engineering model yielded 96.82% classification accuracy with 10-fold cross-validation using the seismic signal dataset. CONCLUSIONS : Our model attained high seismological signal detection performance comparable with more computationally expensive deep learning models. Our handcrafted explainable feature engineering model is computationally less expensive and can be easily implemented. Furthermore, we have introduced a competitive feature engineering model to the deep learning models for the seismic signal classification model.The South African National Library and Information Consortium (SANLiC).https://www.elsevier.com/locate/jagam2024Electrical, Electronic and Computer EngineeringSDG-09: Industry, innovation and infrastructureSDG-13:Climate actio

Delegation and coordination with multiple threshold public goods: experimental evidence

When multiple charities, social programs and community projects simultaneously vie for funding, donors risk mis-coordinating their contributions leading to an inefficient distribution of funding across projects. Community chests and other intermediary organizations facilitate coordination among donors and reduce such risks. To study this, we extend a threshold public goods framework to allow donors to contribute through an intermediary rather than directly to the public goods. Through a series of experiments, we show that the presence of an intermediary increases public good success and subjects’ earnings only when the intermediary is formally committed to direct donations to socially beneficial goods. Without such a restriction, the presence of an intermediary has a negative impact, complicating the donation environment, decreasing contributions and public good success.When multiple charities, social programs and community projects simultaneously vie for funding, donors risk mis-coordinating their contributions leading to an inefficient distribution of funding across projects. Community chests and other intermediary organizations facilitate coordination among donors and reduce such risks. To study this, we extend a threshold public goods framework to allow donors to contribute through an intermediary rather than directly to the public goods. Through a series of experiments, we show that the presence of an intermediary increases public good success and subjects’ earnings only when the intermediary is formally committed to direct donations to socially beneficial goods. Without such a restriction, the presence of an intermediary has a negative impact, complicating the donation environment, decreasing contributions and public good success

Genome-Wide Linkage Scan to Identify Loci Associated with Type 2 Diabetes and Blood Lipid Phenotypes in the Sikh Diabetes Study

In this investigation, we have carried out an autosomal genome-wide linkage analysis to map genes associated with type 2 diabetes (T2D) and five quantitative traits of blood lipids including total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, very low-density lipoprotein (VLDL) cholesterol, and triglycerides in a unique family-based cohort from the Sikh Diabetes Study (SDS). A total of 870 individuals (526 male/344 female) from 321 families were successfully genotyped using 398 polymorphic microsatellite markers with an average spacing of 9.26 cM on the autosomes. Results of non-parametric multipoint linkage analysis using Sall statistics (implemented in Merlin) did not reveal any chromosomal region to be significantly associated with T2D in this Sikh cohort. However, linkage analysis for lipid traits using QTL-ALL analysis revealed promising linkage signals with p≤0.005 for total cholesterol, LDL cholesterol, and HDL cholesterol at chromosomes 5p15, 9q21, 10p11, 10q21, and 22q13. The most significant signal (p = 0.0011) occurred at 10q21.2 for HDL cholesterol. We also observed linkage signals for total cholesterol at 22q13.32 (p = 0.0016) and 5p15.33 (p = 0.0031) and for LDL cholesterol at 10p11.23 (p = 0.0045). Interestingly, some of linkage regions identified in this Sikh population coincide with plausible candidate genes reported in recent genome-wide association and meta-analysis studies for lipid traits. Our study provides the first evidence of linkage for loci associated with quantitative lipid traits at four chromosomal regions in this Asian Indian population from Punjab. More detailed examination of these regions with more informative genotyping, sequencing, and functional studies should lead to rapid detection of novel targets of therapeutic importance

Association of the DRD2 TaqIA, 5-HT1B A-161T, and CNR1 1359 G/A Polymorphisms with Alcohol Dependence

Objective: Alcohol dependence is associated with genetic variants of alcohol-metabolizing enzymes and genes related to dopaminergic, gamma-aminobutyric acidergic, glutamatergic, opioid, cholinergic, and serotonergic systems. Genetic variations in the endogenous cannabinoid system are also involved in alcohol dependence. The present study aimed to evaluate the association between three polymorphisms, DRD2 TaqIA, 5-HT1B A-161T and CNR1 1359 G/A (rs1049353), and alcohol dependence.Methods: One hundred twenty three patients, who were diagnosed as having alcohol dependence according to the DSM-IV criteria and 125 healthy volunteers, were included in the study. With written informed consent, a blood sample was drawn from each individual. Venous blood samples were collected in ethylenediaminetetra acetic acid (EDTA) containing tubes. DNA was extracted from whole blood by the salting out procedure. Genetic analyses were performed as described in the literature by using a Polymerase Chain Reaction method. SPS5 17.0 software was used for statistical analysis.Results: The DRD2 TaglA polymorphism was analyzed in the study and control groups. In the study group, the All A1 genotype was observed in 5 (4.0%) patients, the A1/A2 genotype was observed in 51 (41.5%) patients and the A2/A2 genotype was observed in 67 (54.5%) patients. In the control group, the A1/A1 genotype was observed in 6 (4.8%) subjects, the A1/A2 genotype was observed in 40 (32.0%) subjects and the A2/A2 genotype was observed in 79 (62.2%) subjects. For the 5-HT1B receptor A-161T gene polymorphism, the A/A genotype was detected in 61(49.6%) patients, the Ail genotype was detected in 53 (43.1%) and the T/T genotype was detected in 9 (7.3%) patients. In the control group, the A/A genotype was detected in 84 (67.2%) subjects, the All genotype was detected in 39 (31.2%) subjects, and the T/T genotype was detected only in 2 (1.6%) subjects. The G/G genotype was the most common genotype in both study and control groups for CNR1 1359 gene polymorphism. It was detected in 75 (61.0%) study patients and in 84 (67.2%) control subjects. The G/A genotype was observed in 39 (31.7%) patients of the study group and 38 (30.4%) subjects of the control group. The A/A genotype was the most rare genotype in both groups; it was detected only in 9 (7.3%) study patients and 3 (2.4%) control subjects. Of the three polymorphisms investigated, 5-HT1B A-1 61T was the only one found to be associated with alcohol dependence.Conclusions: The 5-HT1B receptor A-161T polymorphism might be a promising marker for alcohol dependence

Transverse-momentum and pseudorapidity distributions of charged hadrons in pp collisions at √s=7 TeV

This is the pre-print version of the Published Article which can be accessed from the link below.Charged-hadron transverse-momentum and pseudorapidity distributions in proton-proton collisions at √s=7  TeV are measured with the inner tracking system of the CMS detector at the LHC. The charged-hadron yield is obtained by counting the number of reconstructed hits, hit pairs, and fully reconstructed charged-particle tracks. The combination of the three methods gives a charged-particle multiplicity per unit of pseudorapidity dNch/dη||η|<0.5=5.78±0.01(stat)±0.23(syst) for non-single-diffractive events, higher than predicted by commonly used models. The relative increase in charged-particle multiplicity from √s=0.9 to 7 TeV is [66.1±1.0(stat)±4.2(syst)]%. The mean transverse momentum is measured to be 0.545±0.005(stat)±0.015(syst)  GeV/c. The results are compared with similar measurements at lower energies

First measurement of the underlying event activity at the LHC with root s=0.9 TeV

A measurement of the underlying activity in scattering processes with p (T) scale in the GeV region is performed in proton-proton collisions at root s = 0.9 TeV, using data collected by the CMS experiment at the LHC. Charged particle production is studied with reference to the direction of a leading object, either a charged particle or a set of charged particles forming a jet. Predictions of several QCD-inspired models as implemented in PYTHIA are compared, after full detector simulation, to the data. The models generally predict too little production of charged particles with pseudorapidity |eta| LT 2, p (T) GT 0.5 GeV/c, and azimuthal direction transverse to that of the leading object